college+of+engineering
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A Deep-Learned E-Skin Decodes Complex Human Motion
A deep-learning powered single-strained electronic skin sensor can capture human motion from a distance. The single strain sensor placed on the wrist decodes complex five-finger motions in real time with a virtual 3D hand that mirrors the original motions. The deep neural network boosted by rapid situation learning (RSL) ensures stable operation regardless of its position on the surface of the skin.
Conventional approaches require many sensor networks that cover the entire curvilinear surfaces of the target area. Unlike conventional wafer-based fabrication, this laser fabrication provides a new sensing paradigm for motion tracking.
The research team, led by Professor Sungho Jo from the School of Computing, collaborated with Professor Seunghwan Ko from Seoul National University to design this new measuring system that extracts signals corresponding to multiple finger motions by generating cracks in metal nanoparticle films using laser technology. The sensor patch was then attached to a user’s wrist to detect the movement of the fingers.
The concept of this research started from the idea that pinpointing a single area would be more efficient for identifying movements than affixing sensors to every joint and muscle. To make this targeting strategy work, it needs to accurately capture the signals from different areas at the point where they all converge, and then decoupling the information entangled in the converged signals. To maximize users’ usability and mobility, the research team used a single-channeled sensor to generate the signals corresponding to complex hand motions.
The rapid situation learning (RSL) system collects data from arbitrary parts on the wrist and automatically trains the model in a real-time demonstration with a virtual 3D hand that mirrors the original motions. To enhance the sensitivity of the sensor, researchers used laser-induced nanoscale cracking.
This sensory system can track the motion of the entire body with a small sensory network and facilitate the indirect remote measurement of human motions, which is applicable for wearable VR/AR systems.
The research team said they focused on two tasks while developing the sensor. First, they analyzed the sensor signal patterns into a latent space encapsulating temporal sensor behavior and then they mapped the latent vectors to finger motion metric spaces.
Professor Jo said, “Our system is expandable to other body parts. We already confirmed that the sensor is also capable of extracting gait motions from a pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.”
This study was featured in Nature Communications.
Publication:
Kim, K. K., et al. (2020) A deep-learned skin sensor decoding the epicentral human motions. Nature Communications. 11. 2149. https://doi.org/10.1038/s41467-020-16040-y29
Link to download the full-text paper:
https://www.nature.com/articles/s41467-020-16040-y.pdf
Profile: Professor Sungho Jo
shjo@kaist.ac.kr
http://nmail.kaist.ac.kr
Neuro-Machine Augmented Intelligence Lab
School of Computing
College of Engineering
KAIST
2020.06.10 View 10803 -
Professor Jee-Hwan Ryu Receives IEEE ICRA 2020 Outstanding Reviewer Award
Professor Jee-Hwan Ryu from the Department of Civil and Environmental Engineering was selected as this year’s winner of the Outstanding Reviewer Award presented by the Institute of Electrical and Electronics Engineers International Conference on Robotics and Automation (IEEE ICRA). The award ceremony took place on June 5 during the conference that is being held online May 31 through August 31 for three months.
The IEEE ICRA Outstanding Reviewer Award is given every year to the top reviewers who have provided constructive and high-quality thesis reviews, and contributed to improving the quality of papers published as results of the conference.
Professor Ryu was one of the four winners of this year’s award. He was selected from 9,425 candidates, which was approximately three times bigger than the candidate pool in previous years. He was strongly recommended by the editorial committee of the conference.
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2020.06.10 View 7384 -
Unravelling Complex Brain Networks with Automated 3-D Neural Mapping
-Automated 3-D brain imaging data analysis technology offers more reliable and standardized analysis of the spatial organization of complex neural circuits.-
KAIST researchers developed a new algorithm for brain imaging data analysis that enables the precise and quantitative mapping of complex neural circuits onto a standardized 3-D reference atlas.
Brain imaging data analysis is indispensable in the studies of neuroscience. However, analysis of obtained brain imaging data has been heavily dependent on manual processing, which cannot guarantee the accuracy, consistency, and reliability of the results.
Conventional brain imaging data analysis typically begins with finding a 2-D brain atlas image that is visually similar to the experimentally obtained brain image. Then, the region-of-interest (ROI) of the atlas image is matched manually with the obtained image, and the number of labeled neurons in the ROI is counted.
Such a visual matching process between experimentally obtained brain images and 2-D brain atlas images has been one of the major sources of error in brain imaging data analysis, as the process is highly subjective, sample-specific, and susceptible to human error. Manual analysis processes for brain images are also laborious, and thus studying the complete 3-D neuronal organization on a whole-brain scale is a formidable task.
To address these issues, a KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering developed new brain imaging data analysis software named 'AMaSiNe (Automated 3-D Mapping of Single Neurons)', and introduced the algorithm in the May 26 issue of Cell Reports.
AMaSiNe automatically detects the positions of single neurons from multiple brain images, and accurately maps all the data onto a common standard 3-D reference space. The algorithm allows the direct comparison of brain data from different animals by automatically matching similar features from the images, and computing the image similarity score.
This feature-based quantitative image-to-image comparison technology improves the accuracy, consistency, and reliability of analysis results using only a small number of brain slice image samples, and helps standardize brain imaging data analyses.
Unlike other existing brain imaging data analysis methods, AMaSiNe can also automatically find the alignment conditions from misaligned and distorted brain images, and draw an accurate ROI, without any cumbersome manual validation process.
AMaSiNe has been further proved to produce consistent results with brain slice images stained utilizing various methods including DAPI, Nissl, and autofluorescence.
The two co-lead authors of this study, Jun Ho Song and Woochul Choi, exploited these benefits of AMaSiNe to investigate the topographic organization of neurons that project to the primary visual area (VISp) in various ROIs, such as the dorsal lateral geniculate nucleus (LGd), which could hardly be addressed without proper calibration and standardization of the brain slice image samples.
In collaboration with Professor Seung-Hee Lee's group of the Department of Biological Science, the researchers successfully observed the 3-D topographic neural projections to the VISp from LGd, and also demonstrated that these projections could not be observed when the slicing angle was not properly corrected by AMaSiNe. The results suggest that the precise correction of a slicing angle is essential for the investigation of complex and important brain structures.
AMaSiNe is widely applicable in the studies of various brain regions and other experimental conditions. For example, in the research team’s previous study jointly conducted with Professor Yang Dan’s group at UC Berkeley, the algorithm enabled the accurate analysis of the neuronal subsets in the substantia nigra and their projections to the whole brain. Their findings were published in Science on January 24.
AMaSiNe is of great interest to many neuroscientists in Korea and abroad, and is being actively used by a number of other research groups at KAIST, MIT, Harvard, Caltech, and UC San Diego.
Professor Paik said, “Our new algorithm allows the spatial organization of complex neural circuits to be found in a standardized 3-D reference atlas on a whole-brain scale. This will bring brain imaging data analysis to a new level.”
He continued, “More in-depth insights for understanding the function of brain circuits can be achieved by facilitating more reliable and standardized analysis of the spatial organization of neural circuits in various regions of the brain.”
This work was supported by KAIST and the National Research Foundation of Korea (NRF).
Figure and Image Credit: Professor Se-Bum Paik, KAIST
Figure and Image Usage Restrictions: News organizations may use or redistribute these figures and images, with proper attribution, as part of news coverage of this paper only.
Publication:
Song, J. H., et al. (2020). Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex. Cell Reports. Volume 31, 107682. Available online at https://doi.org/10.1016/j.celrep.2020.107682
Profile:
Se-Bum Paik
Assistant Professor
sbpaik@kaist.ac.kr
http://vs.kaist.ac.kr/
VSNN Laboratory
Department of Bio and Brain Engineering
Program of Brain and Cognitive Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2020.06.08 View 11911 -
‘Mole-bot’ Optimized for Underground and Space Exploration
Biomimetic drilling robot provides new insights into the development of efficient drilling technologies
Mole-bot, a drilling biomimetic robot designed by KAIST, boasts a stout scapula, a waist inclinable on all sides, and powerful forelimbs. Most of all, the powerful torque from the expandable drilling bit mimicking the chiseling ability of a mole’s front teeth highlights the best feature of the drilling robot.
The Mole-bot is expected to be used for space exploration and mining for underground resources such as coalbed methane and Rare Earth Elements (REE), which require highly advanced drilling technologies in complex environments.
The research team, led by Professor Hyun Myung from the School of Electrical Engineering, found inspiration for their drilling bot from two striking features of the African mole-rat and European mole.
“The crushing power of the African mole-rat’s teeth is so powerful that they can dig a hole with 48 times more power than their body weight. We used this characteristic for building the main excavation tool. And its expandable drill is designed not to collide with its forelimbs,” said Professor Myung.
The 25-cm wide and 84-cm long Mole-bot can excavate three times faster with six times higher directional accuracy than conventional models. The Mole-bot weighs 26 kg.
After digging, the robot removes the excavated soil and debris using its forelimbs. This embedded muscle feature, inspired by the European mole’s scapula, converts linear motion into a powerful rotational force. For directional drilling, the robot’s elongated waist changes its direction 360° like living mammals.
For exploring underground environments, the research team developed and applied new sensor systems and algorithms to identify the robot’s position and orientation using graph-based 3D Simultaneous Localization and Mapping (SLAM) technology that matches the Earth’s magnetic field sequence, which enables 3D autonomous navigation underground.
According to Market & Market’s survey, the directional drilling market in 2016 is estimated to be 83.3 billion USD and is expected to grow to 103 billion USD in 2021. The growth of the drilling market, starting with the Shale Revolution, is likely to expand into the future development of space and polar resources. As initiated by Space X recently, more attention for planetary exploration will be on the rise and its related technology and equipment market will also increase.
The Mole-bot is a huge step forward for efficient underground drilling and exploration technologies. Unlike conventional drilling processes that use environmentally unfriendly mud compounds for cleaning debris, Mole-bot can mitigate environmental destruction. The researchers said their system saves on cost and labor and does not require additional pipelines or other ancillary equipment.
“We look forward to a more efficient resource exploration with this type of drilling robot. We also hope Mole-bot will have a very positive impact on the robotics market in terms of its extensive application spectra and economic feasibility,” said Professor Myung.
This research, made in collaboration with Professor Jung-Wuk Hong and Professor Tae-Hyuk Kwon’s team in the Department of Civil and Environmental Engineering for robot structure analysis and geotechnical experiments, was supported by the Ministry of Trade, Industry and Energy’s Industrial Technology Innovation Project.
Profile
Professor Hyun Myung
Urban Robotics Lab
http://urobot.kaist.ac.kr/
School of Electrical Engineering
KAIST
2020.06.05 View 9169 -
A New Strategy for the Optimal Electroreduction of CO2 to High-Value Products
-Researchers suggest that modulation of local CO2 concentration improves the selectivity, conversion rate, and electrode stability, and shed a new light on the electrochemical CO2 reduction technology for controlling emissions at a low cost.-
A KAIST research team presented three novel approaches for modulating local carbon dioxide (CO2) concentration in gas-diffusion electrode (GDE)-based flow electrolyzers. Their study also empirically demonstrated that providing a moderate local CO2 concentration is effective in promoting Carbon–Carbon (C–C) coupling reactions toward the production of multi-carbon molecules. This work, featured in the May 20th issue of Joule, serves as a rational guide to tune CO2 mass transport for the optimal production of valuable multi-carbon products.
Amid global efforts to reduce and recycle anthropogenic CO2 emissions, CO2 electrolysis holds great promise for converting CO2 into useful chemicals that were traditionally derived from fossil fuels. Many researches have been attempting to improve the selectivity of CO2 for commercially and industrially high-value multi-carbon products such as ethylene, ethanol, and 1-propanol, due to their high energy density and large market size.
In order to achieve the highly-selective conversion of CO2 into valuable multi-carbon products, past studies have focused on the design of catalysts and the tuning of local environment related to pH, cations, and molecular additives.
Conventional CO2 electrolytic systems relied heavily on an alkaline electrolyte that is often consumed in large quantities when reacting with CO2, and thus led to an increase in the operational costs. Moreover, the life span of a catalyst electrode was short, due to its inherent chemical reactivity.
In their recent study, a group of KAIST researchers led by Professor Jihun Oh from the Department of Materials Science and Engineering reported that the local CO2 concentration has been an overlooked factor that largely affects the selectivity toward multi-carbon products.
Professor Oh and his researchers Dr. Ying Chuan Tan, Hakhyeon Song, and Kelvin Berm Lee proposed that there is an intimate relation between local CO2 and multi-carbon product selectivity during electrochemical CO2 reduction reactions. The team employed the mass-transport modeling of a GDE-based flow electrolyzer that utilizes copper oxide (Cu2O) nanoparticles as model catalysts. They then identified and applied three approaches to modulate the local CO2 concentration within a GDE-based electrolytic system, including 1) controlling the catalyst layer structure, 2) CO2 feed concentration, and 3) feed flow rate.
Contrary to common intuition, the study showed that providing a maximum CO2 transport leads to suboptimal multi-carbon product faradaic efficiency. Instead, by restricting and providing a moderate local CO2 concentration, C–C coupling can be significantly enhanced.
The researchers demonstrated experimentally that the selectivity rate increased from 25.4% to 61.9%, and from 5.9% to 22.6% for the CO2 conversion rate. When a cheap milder near-neutral electrolyte was used, the stability of the CO2 electrolytic system improved to a great extent, allowing over 10 hours of steady selective production of multi-carbon products.
Dr. Tan, the lead author of the paper, said, “Our research clearly revealed that the optimization of the local CO2 concentration is the key to maximizing the efficiency of converting CO2 into high-value multi-carbon products.”
Professor Oh added, “This finding is expected to deliver new insights to the research community that variables affecting local CO2 concentration are also influential factors in the electrochemical CO2 reduction reaction performance. My colleagues and I hope that our study becomes a cornerstone for related technologies and their industrial applications.”
This work was supported by the Korean Ministry of Science and ICT (MSIT) Creative Materials Discovery Program.
Publication:
Tan, Y. C et al. (2020) ‘Modulating Local CO2 Concentration as a General Strategy for Enhancing C−C Coupling in CO2 Electroreduction’, Joule, Vol. 4, Issue 5, pp. 1104-1120. Available online at https://doi.org/10.1016/j.joule.2020.03.013
Profile: Jihun Oh, PhD
Associate Professor
jihun.oh@kaist.ac.kr
http://les.kaist.ac.kr/
Laboratory for Energy and Sustainability (LE&S)
Department of Materials Science and Engineering (MSE)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Republic of Korea
Profile: Ying Chuan Tan, PhD
tanyc@kaist.ac.kr
LE&S, MSE, KAIST
Profile: Hakhyeon Song, PhD Candidate
hyeon0401@kaist.ac.kr
LE&S, MSE, KAIST
Profile: Kelvin Berm Lee, M.S. Candidate
kbl9105@kaist.ac.kr
LE&S, MSE, KAIST
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2020.06.03 View 10699 -
From Dark to Light in a Flash: Smart Film Lets Windows Switch Autonomously
Researchers have developed a new easy-to-use smart optical film technology that allows smart window devices to autonomously switch between transparent and opaque states in response to the surrounding light conditions.
The proposed 3D hybrid nanocomposite film with a highly periodic network structure has empirically demonstrated its high speed and performance, enabling the smart window to quantify and self-regulate its high-contrast optical transmittance. As a proof of concept, a mobile-app-enabled smart window device for Internet of Things (IoT) applications has been realized using the proposed smart optical film with successful expansion to the 3-by-3-inch scale. This energy-efficient and cost-effective technology holds great promise for future use in various applications that require active optical transmission modulation.
Flexible optical transmission modulation technologies for smart applications including privacy-protection windows, zero-energy buildings, and beam projection screens have been in the spotlight in recent years. Conventional technologies that used external stimuli such as electricity, heat, or light to modulate optical transmission had only limited applications due to their slow response speeds, unnecessary color switching, and low durability, stability, and safety.
The optical transmission modulation contrast achieved by controlling the light scattering interfaces on non-periodic 2D surface structures that often have low optical density such as cracks, wrinkles, and pillars is also generally low. In addition, since the light scattering interfaces are exposed and not subject to any passivation, they can be vulnerable to external damage and may lose optical transmission modulation functions. Furthermore, in-plane scattering interfaces that randomly exist on the surface make large-area modulation with uniformity difficult.
Inspired by these limitations, a KAIST research team led by Professor Seokwoo Jeon from the Department of Materials Science and Engineering and Professor Jung-Wuk Hong of the Civil and Environmental Engineering Department used proximity-field nanopatterning (PnP) technology that effectively produces highly periodic 3D hybrid nanostructures, and an atomic layer deposition (ALD) technique that allows the precise control of oxide deposition and the high-quality fabrication of semiconductor devices.
The team then successfully produced a large-scale smart optical film with a size of 3 by 3 inches in which ultrathin alumina nanoshells are inserted between the elastomers in a periodic 3D nanonetwork.
This “mechano-responsive” 3D hybrid nanocomposite film with a highly periodic network structure is the largest smart optical transmission modulation film that exists. The film has been shown to have state-of-the-art optical transmission modulation of up to 74% at visible wavelengths from 90% initial transmission to 16% in the scattering state under strain. Its durability and stability were proved by more than 10,000 tests of harsh mechanical deformation including stretching, releasing, bending, and being placed under high temperatures of up to 70°C. When this film was used, the transmittance of the smart window device was adjusted promptly and automatically within one second in response to the surrounding light conditions. Through these experiments, the underlying physics of optical scattering phenomena occurring in the heterogeneous interfaces were identified. Their findings were reported in the online edition of Advanced Science on April 26. KAIST Professor Jong-Hwa Shin’s group and Professor Young-Seok Shim at Silla University also collaborated on this project.
Donghwi Cho, a PhD candidate in materials science and engineering at KAIST and co-lead author of the study, said, “Our smart optical film technology can better control high-contrast optical transmittance by relatively simple operating principles and with low energy consumption and costs.”
“When this technology is applied by simply attaching the film to a conventional smart window glass surface without replacing the existing window system, fast switching and uniform tinting are possible while also securing durability, stability, and safety. In addition, its wide range of applications for stretchable or rollable devices such as wall-type displays for a beam projection screen will also fulfill aesthetic needs,” he added.
This work was supported by the National Research Foundation of Korea (NRF), and the Korean Ministries of Science, ICT and Future Planning (MSIP), and Science and ICT (MSIT).
Publication:
Cho, D, et al. (2020) ‘High-Contrast Optical Modulation from Strain-Indicated Nanogaps at 3D Heterogeneous Interfaces’ Advanced Science, 1903708. Available online at https://doi.org/10.1002/advs.201903708
Profile: Seokwoo Jeon, PhD
Professor
jeon39@kaist.ac.kr
https://fdml.kaist.ac.kr/
Flexible Device and Metamaterials Lab (FDML)
Department of Materials Science and Engineering (MSE)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.krDaejeon 34141, Korea
Profile: Jung-Wuk Hong, PhD
Associate Professor
j.hong@kaist.ac.kr
http://aaml.kaist.ac.kr
Advanced Applied Mechanics Laboratory (AAML)
Department of Civil and Environmental Engineering
KAIST
Profile: Donghwi Cho
PhD Candidate
roy0202@kaist.ac.krFDML, MSE, KAIST
Profile: Young-Seok Shim, PhD
Assistant Professor
ysshim@silla.ac.kr
Division of Materials Science and Engineering Silla University
https://www.silla.ac.kr
Busan 46958, Korea
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2020.06.02 View 11572 -
Professor Dongsu Han Named Program Chair for ACM CoNEXT 2020
Professor Dongsu Han from the School of Electrical Engineering has been appointed as the program chair for the 16th Association for Computing Machinery’s International Conference on emerging Networking EXperiments and Technologies (ACM CoNEXT 2020). Professor Han is the first program chair to be appointed from an Asian institution.
ACM CoNEXT is hosted by ACM SIGCOMM, ACM's Special Interest Group on Data Communications, which specializes in the field of communication and computer networks.
Professor Han will serve as program co-chair along with Professor Anja Feldmann from the Max Planck Institute for Informatics. Together, they have appointed 40 world-leading researchers as program committee members for this conference, including Professor Song Min Kim from KAIST School of Electrical Engineering.
Paper submissions for the conference can be made by the end of June, and the event itself is to take place from the 1st to 4th of December.
Conference Website: https://conferences2.sigcomm.org/co-next/2020/#!/home
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2020.06.02 View 8414 -
Universal Virus Detection Platform to Expedite Viral Diagnosis
Reactive polymer-based tester pre-screens dsRNAs of a wide range of viruses without their genome sequences
The prompt, precise, and massive detection of a virus is the key to combat infectious diseases such as Covid-19. A new viral diagnostic strategy using reactive polymer-grafted, double-stranded RNAs will serve as a pre-screening tester for a wide range of viruses with enhanced sensitivity.
Currently, the most widely using viral detection methodology is polymerase chain reaction (PCR) diagnosis, which amplifies and detects a piece of the viral genome. Prior knowledge of the relevant primer nucleic acids of the virus is quintessential for this test.
The detection platform developed by KAIST researchers identifies viral activities without amplifying specific nucleic acid targets. The research team, co-led by Professor Sheng Li and Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering, constructed a universal virus detection platform by utilizing the distinct features of the PPFPA-grafted surface and double-stranded RNAs.
The key principle of this platform is utilizing the distinct feature of reactive polymer-grafted surfaces, which serve as a versatile platform for the immobilization of functional molecules. These activated surfaces can be used in a wide range of applications including separation, delivery, and detection. As long double-stranded RNAs are common byproducts of viral transcription and replication, these PPFPA-grafted surfaces can detect the presence of different kinds of viruses without prior knowledge of their genomic sequences.
“We employed the PPFPA-grafted silicon surface to develop a universal virus detection platform by immobilizing antibodies that recognize double-stranded RNAs,” said Professor Kim.
To increase detection sensitivity, the research team devised two-step detection process analogues to sandwich enzyme-linked immunosorbent assay where the bound double-stranded RNAs are then visualized using fluorophore-tagged antibodies that also recognize the RNAs’ double-stranded secondary structure.
By utilizing the developed platform, long double-stranded RNAs can be detected and visualized from an RNA mixture as well as from total cell lysates, which contain a mixture of various abundant contaminants such as DNAs and proteins.
The research team successfully detected elevated levels of hepatitis C and A viruses with this tool.
“This new technology allows us to take on virus detection from a new perspective. By targeting a common biomarker, viral double-stranded RNAs, we can develop a pre-screening platform that can quickly differentiate infected populations from non-infected ones,” said Professor Li.
“This detection platform provides new perspectives for diagnosing infectious diseases. This will provide fast and accurate diagnoses for an infected population and prevent the influx of massive outbreaks,” said Professor Kim.
This work is featured in Biomacromolecules. This work was supported by the Agency for Defense Development (Grant UD170039ID), the Ministry of Science and ICT (NRF-2017R1D1A1B03034660, NRF-2019R1C1C1006672), and the KAIST Future Systems Healthcare Project from the Ministry of Science and ICT (KAISTHEALTHCARE42).
Profile:-Professor Yoosik KimDepartment of Chemical and Biomolecular Engineeringhttps://qcbio.kaist.ac.kr
KAIST-Professor Sheng LiDepartment of Chemical and Biomolecular Engineeringhttps://bcpolymer.kaist.ac.kr
KAIST
Publication:Ku et al., 2020. Reactive Polymer Targeting dsRNA as Universal Virus Detection Platform with Enhanced Sensitivity. Biomacromolecules (https://doi.org/10.1021/acs.biomac.0c00379).
2020.06.01 View 17116 -
KAIST Elected to Universities Space Research Association Membership
KAIST joined the Universities Space Research Association (USRA) on May 4, and brought the Association to a total of 113 member universities. The expertise KAIST brings will broaden the Association’s collective strength in space-related science, technology, and engineering worldwide. Professor Hyosang Yoon from the Department of Aerospace Engineering will serve as the representative of KAIST to USRA.
KAIST was selected by USRA’s current university members, in recognition of its significant commitment in, and contributions to, the fields of space and aerospace research. Especially, KAIST have developed Korea's first satellite, KITSAT-1 in 1992, which paved the way for space research in Korea and helped the nation strengthen technological competitiveness in that field.
USRA was established in 1969 under the auspices of the National Academy of Sciences (NAS) of the United States. It is a non-profit corporation chartered to advance space-related science, technology, and engineering. USRA operates scientific institutes and facilities, and conducts other major research and educational programs, using federal funding. USRA also engages the university community and employs in-house scientific leadership, innovative research and development, and project management expertise.
USRA’s President and CEO Dr. Jeffrey A. Isaacson said in his announcement, “We are delighted to welcome these two renowned universities as members. We look forward to their active engagement with, and contributions to, our Association.” President Isaacson visited KAIST on December 10 last year to discuss possible collaborations between two organizations.
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2020.05.29 View 5946 -
Professor Sue-Hyun Lee Listed Among WEF 2020 Young Scientists
Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering joined the World Economic Forum (WEF)’s Young Scientists Community on May 26. The class of 2020 comprises 25 leading researchers from 14 countries across the world who are at the forefront of scientific problem-solving and social change. Professor Lee was the only Korean on this year’s roster.
The WEF created the Young Scientists Community in 2008 to engage leaders from the public and private sectors with science and the role it plays in society. The WEF selects rising-star academics, 40 and under, from various fields every year, and helps them become stronger ambassadors for science, especially in tackling pressing global challenges including cybersecurity, climate change, poverty, and pandemics.
Professor Lee is researching how memories are encoded, recalled, and updated, and how emotional processes affect human memory, in order to ultimately direct the development of therapeutic methods to treat mental disorders. She has made significant contributions to resolving ongoing debates over the maintenance and changes of memory traces in the brain.
In recognition of her research excellence, leadership, and commitment to serving society, the President and the Dean of the College of Engineering at KAIST nominated Professor Lee to the WEF’s Class of 2020 Young Scientists Selection Committee. The Committee also acknowledged Professor Lee’s achievements and potential for expanding the boundaries of knowledge and practical applications of science, and accepted her into the Community.
During her three-year membership in the Community, Professor Lee will be committed to participating in WEF-initiated activities and events related to promising therapeutic interventions for mental disorders and future directions of artificial intelligence.
Seven of this year’s WEF Young Scientists are from Asia, including Professor Lee, while eight are based in Europe. Six study in the Americas, two work in South Africa, and the remaining two in the Middle East. Fourteen, more than half, of the newly announced 25 Young Scientists are women.
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2020.05.26 View 9969 -
Professor Tek-jin Nam Elected to DSR Int’l Advisory Council
Professor Tek-jin Nam from the Department of Industrial Design was elected to serve on the first International Advisory Council (IAC) of the Design Research Society (DRS).
The DRS, an academic society in the field of design research, was founded in the UK in 1966 with the mission of developing and promoting design research. The IAC is newly established under the new DRS governance structure, and its members are selected from distinguished design researchers recommended by DRS members around the globe.
The new IAC members will carry out various activities offered by the DRS, which include innovating design research, strengthening the design researchers’ network and developing policies to nurture new researchers.
2020.05.22 View 4479 -
Visualization of Functional Components to Characterize Optimal Composite Electrodes
Researchers have developed a visualization method that will determine the distribution of components in battery electrodes using atomic force microscopy. The method provides insights into the optimal conditions of composite electrodes and takes us one step closer to being able to manufacture next-generation all-solid-state batteries.
Lithium-ion batteries are widely used in smart devices and vehicles. However, their flammability makes them a safety concern, arising from potential leakage of liquid electrolytes.
All-solid-state lithium ion batteries have emerged as an alternative because of their better safety and wider electrochemical stability. Despite their advantages, all-solid-state lithium ion batteries still have drawbacks such as limited ion conductivity, insufficient contact areas, and high interfacial resistance between the electrode and solid electrolyte.
To solve these issues, studies have been conducted on composite electrodes in which lithium ion conducting additives are dispersed as a medium to provide ion conductive paths at the interface and increase the overall ionic conductivity.
It is very important to identify the shape and distribution of the components used in active materials, ion conductors, binders, and conductive additives on a microscopic scale for significantly improving the battery operation performance.
The developed method is able to distinguish regions of each component based on detected signal sensitivity, by using various modes of atomic force microscopy on a multiscale basis, including electrochemical strain microscopy and lateral force microscopy.
For this research project, both conventional electrodes and composite electrodes were tested, and the results were compared. Individual regions were distinguished and nanoscale correlation between ion reactivity distribution and friction force distribution within a single region was determined to examine the effect of the distribution of binder on the electrochemical strain.
The research team explored the electrochemical strain microscopy amplitude/phase and lateral force microscopy friction force dependence on the AC drive voltage and the tip loading force, and used their sensitivities as markers for each component in the composite anode.
This method allows for direct multiscale observation of the composite electrode in ambient condition, distinguishing various components and measuring their properties simultaneously.
Lead author Dr. Hongjun Kim said, “It is easy to prepare the test sample for observation while providing much higher spatial resolution and intensity resolution for detected signals.” He added, “The method also has the advantage of providing 3D surface morphology information for the observed specimens.”
Professor Seungbum Hong from the Department of Material Sciences and Engineering said, “This analytical technique using atomic force microscopy will be useful for quantitatively understanding what role each component of a composite material plays in the final properties.”
“Our method not only will suggest the new direction for next-generation all-solid-state battery design on a multiscale basis but also lay the groundwork for innovation in the manufacturing process of other electrochemical materials.”
This study is published in ACS Applied Energy Materials and supported by the Big Science Research and Development Project under the Ministry of Science and ICT and the National Research Foundation of Korea, the Basic Research Project under the Wearable Platform Materials Technology Center, and KAIST Global Singularity Research Program for 2019 and 2020.
Publication:Kim, H, et al. (2020) ‘Visualization of Functional Components in a Lithium Silicon Titanium Phosphate-Natural Graphite Composite Anode’. ACS Applied Energy Materials, Volume 3, Issue 4, pp. 3253-3261. Available online at https://doi.org/10.1021/acsaem.9b02045
Profile:
Seungbum Hong
Professor
seungbum@kaist.ac.kr
http://mii.kaist.ac.kr/
Materials Imaging and Integration Laboratory
Department of Material Sciences and Engineering
KAIST
2020.05.22 View 8694